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Análisis de comportamiento (Behavioral Analysis)
Discover how behavioral analysis can transform credit scoring by predicting future repayment reliability.
Discover how behavioral analysis can transform credit scoring by predicting future repayment reliability.
Traditional credit scoring models rely on a consumer's financial history. This includes previous credit transactions, late payments made, preferred types of credit products, total amounts owed, and more.
What if credit scoring didn’t just assess an applicant's past, but could predict their future? Imagine the ability to foresee how reliably they will repay a new loan—or whether they will repay it at all.
Modern lending organizations can achieve this by using a credit risk assessment analysis of a potential borrower's behavior.
So, what is behavior analysis in the context of credit scoring?
This term covers tracking a potential borrower's financial habits and behavioral traits to assess their creditworthiness.
Unlike traditional credit scoring, behavioral data analysis allows you to determine how a person manages their finances and detect even minor anomalies in real-time.
This capability is what makes this method so popular among lenders.
Fortune Business Insights said the behavioral analytics market was valued at $801.8 million in 2023. The same source predicts it will reach $11.5 billion by 2032.
The second place in terms of the prevalence of behavior analysis belongs to the BFSI sector (banking, financial services, and insurance).
It is possible to track and analyze human behavior manually. However, this method is time-consuming and of questionable accuracy.
It is much more efficient to turn to automated behavioral analysis, relying on advanced modern technologies.
Automated behavioral analysis leverages machine learning (ML) and advanced systems built on this technology.
This approach is fully justified, as machine learning algorithms provide high accuracy in detecting fraudulent behavior. According to Research Gate, it reaches 70 to 80 percent.
Another advantage of automated fraud behavior detection is that systems based on machine learning can learn in real time. This allows them to adapt to changing fraudulent tactics and detect misconduct based on the online behavior of the applicant.
To improve the effectiveness of credit decisioning by accurately identifying fraud behavior, several indicators need to be analyzed.
1. Location of the borrower. To do this, consider the borrower's address listed on the loan application, their IP address, and geolocation tags on social networks.
Any inconsistencies in these data can be alarming.
For example, a sudden change in the applicant's location. Perhaps their data was stolen, and the loan application was not submitted by them at all.
2. The device used. If the user usually logged into his account from an Apple device, but this time connected from Samsung, it can be a red flag.
3. The timing of contacting the lending organization. Fintech companies often provide their services around the clock. However, average consumers typically resolve their financial issues during the day. If an application is received at night, this is a reason to pay extra attention to the applicant.
4. The financial behavior of the potential borrower. Every consumer has buying habits. They spend a certain amount per month on groceries, clothing, household needs, etc.
If, however, the applicant has recently made several large online purchases, money transfers, or opened several new accounts, such activity may be suspicious.
5. Connection features. Assessing a potential borrower typically involves checking their IP address. If such a check reveals the use of a VPN or proxy, this may be considered fraudulent behavior. After all, criminals often use various methods of anonymization on the web.
Combining traditional and behavioral credit scoring allows lenders to achieve the following results:
1. Increased objectivity in assessing creditworthiness. Behavioral analysis allows not only historical data but also online information to be taken into account.
2. Lending to people with limited or no credit history. Thanks to behavioral analysis, recent immigrants, young people, and people from developing countries can count on getting a loan. In short, all unbanked and underbanked segments of the population.
3. Reducing the risk of default. This method of assessing applicants identifies unreliable borrowers and adjusts lending models to reduce risk.
4. Improved fraud detection. Behavioral analytics can detect the smallest anomalies in borrowers' behavior to suspect criminal intentions.
Automated behavioral analysis is an effective way to optimize credit risk management.
The accuracy of the analysis directly depends on the quality of the data used to train the machine learning models.
Therefore, it is essential to use reliable alternative data providers to achieve the desired results.
RiskSeal builds a full 360-degree user profile. It combines digital credit scores, trust scores, and various behavioral and solvency metrics for a complete evaluation.
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